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Signal and Image Processing

Module name (EN):
Name of module in study programme. It should be precise and clear.
Signal and Image Processing
Degree programme:
Study Programme with validity of corresponding study regulations containing this module.
Electrical Engineering and Information Technology, Bachelor, ASPO 01.10.2018
Module code: E2504
SAP-Submodule-No.:
The exam administration creates a SAP-Submodule-No for every exam type in every module. The SAP-Submodule-No is equal for the same module in different study programs.
P211-0128
Hours per semester week / Teaching method:
The count of hours per week is a combination of lecture (V for German Vorlesung), exercise (U for Übung), practice (P) oder project (PA). For example a course of the form 2V+2U has 2 hours of lecture and 2 hours of exercise per week.
3V+1U (4 hours per week)
ECTS credits:
European Credit Transfer System. Points for successful completion of a course. Each ECTS point represents a workload of 30 hours.
5
Semester: 5
Mandatory course: yes
Language of instruction:
German
Assessment:
Written exam

[updated 08.01.2020]
Applicability / Curricular relevance:
All study programs (with year of the version of study regulations) containing the course.

E2504 (P211-0128) Electrical Engineering and Information Technology, Bachelor, ASPO 01.10.2018 , semester 5, mandatory course, technical
Workload:
Workload of student for successfully completing the course. Each ECTS credit represents 30 working hours. These are the combined effort of face-to-face time, post-processing the subject of the lecture, exercises and preparation for the exam.

The total workload is distributed on the semester (01.04.-30.09. during the summer term, 01.10.-31.03. during the winter term).
60 class hours (= 45 clock hours) over a 15-week period.
The total student study time is 150 hours (equivalent to 5 ECTS credits).
There are therefore 105 hours available for class preparation and follow-up work and exam preparation.
Recommended prerequisites (modules):
None.
Recommended as prerequisite for:
Module coordinator:
Prof. Dr. Michael Kleer
Lecturer: Prof. Dr. Michael Kleer

[updated 10.09.2018]
Learning outcomes:
After successfully completing this course, students will be able to apply system theory to image processing problems. They will have acquired the skills and abilities necessary for designing and implementing image processing systems. They will be familiar with the hard and software used for image processing and be able to convert it into systems. Students will be able to independently understand a task from the field of optical quality assurance in the broadest sense, find solutions and put these solutions into action. Our primary focus will be on the application of these skills. Students will be able to adapt known solutions to new tasks and come up with new combinations of methods.

[updated 08.01.2020]
Module content:
1. One-dimensional signals in the time domain, mathematical description, representation of associated spectra, explanation of filter process, transition to discrete signals and discrete spectra, sampling, FFT 2. Two-dimensional signals, extending mathematical theory 3. Images as two-dimensional signals in the spatial domain, simple key figures for images, quantization and rasterization of images, discrete image processing algorithms in the spatial domain 4. Image processing algorithms in the frequency domain

[updated 08.01.2020]
Teaching methods/Media:
Presentation, blackboard, lecture notes

[updated 08.01.2020]
Recommended or required reading:
Gonzalez, Rafael C.; Woods, Richard E.: Digital Image Processing, Pearson, (latest edition) Pratt, W.K.: Digital Image Processing, Wiley, 1991, 2nd Ed. Rosenfeld, Azriel; Kak, Avinash C.: Digital Picture Processing, Vol. 1+2, Academic Press Wahl, Friedrich M.: Digitale Bildsignalverarbeitung, Springer, 1989

[updated 08.01.2020]
 
[Wed Dec  4 16:40:01 CET 2024, CKEY=e3E2504, BKEY=ei, CID=E2504, LANGUAGE=en, DATE=04.12.2024]